{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-layer FNN on MNIST\n",
    "\n",
    "This is MLP (784-X^W-10) on MNIST. SGD algorithm (lr=0.1) with 100 epoches.\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import *\n",
    "import locale\n",
    "locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as font_manager\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\" Extract final stats from resman's diary file\"\"\"\n",
    "def extract_num(lines0, is_reg=False):\n",
    "\n",
    "    if is_reg:\n",
    "        valid_loss_str     = lines0[-5]\n",
    "        valid_accuracy_str = lines0[-6]\n",
    "        train_loss_str     = lines0[-8]\n",
    "        train_accuracy_str = lines0[-9]\n",
    "        average_time_str   = lines0[-10]        \n",
    "        run_time_str       = lines0[-11]   \n",
    "        \n",
    "    else: \n",
    "        valid_loss_str     = lines0[-6]\n",
    "        valid_accuracy_str = lines0[-7]\n",
    "        train_loss_str     = lines0[-10]\n",
    "        train_accuracy_str = lines0[-11]\n",
    "        average_time_str   = lines0[-12]        \n",
    "        run_time_str       = lines0[-13]\n",
    "\n",
    "\n",
    "    valid_loss     = float(valid_loss_str.split( )[-1])\n",
    "    valid_accuracy = float(valid_accuracy_str.split( )[-1])\n",
    "    train_loss     = float(train_loss_str.split( )[-1])\n",
    "    train_accuracy = float(train_accuracy_str.split( )[-1])\n",
    "    run_time       = float(run_time_str.split( )[-1])\n",
    "    \n",
    "    return valid_loss, valid_accuracy, train_loss, train_accuracy, run_time\n",
    "\n",
    "\"\"\" Extract number of total parameters for each net config from resman's diary file\"\"\"\n",
    "def parse_num_params(lines0):\n",
    "    line_str = ''.join(lines0)\n",
    "    idx = line_str.find(\"Total params\")\n",
    "    param_str = line_str[idx+14:idx+14+20] # 14 is the length of string \"Total params: \"\n",
    "    param_num = param_str.split(\"\\n\")[0]\n",
    "    return int(locale.atof(param_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract results from diary file\n",
    "\n",
    "    1. Number of params\n",
    "    2. Loss/Accuarcy for training/testing\n",
    "    3. Runing time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "results_dir = '../results/fnn_mnist_permuted_label_t500_all_high'\n",
    "       \n",
    "depth = [2]# [1,2,3,4,5]\n",
    "width = [400]# [50,100,200,400]\n",
    "dim   = [0,1000,10000,20000,30000,40000,50000,60000,70000,80000,90000,100000,110000,120000,130000,140000,150000,160000,170000,180000,190000,200000,210000,220000,230000,240000,250000,260000,270000,280000,290000,300000]\n",
    "# dim =[0]\n",
    "\n",
    "########## 1. filename list of diary ########################\n",
    "diary_names = []\n",
    "for subdir, dirs, files in os.walk(results_dir):\n",
    "    for file in files:\n",
    "        \n",
    "        if file == 'diary':\n",
    "            fname = os.path.join(subdir, file)\n",
    "            diary_names.append(fname)\n",
    "            \n",
    "\n",
    "########## 2. Construct stats (width, depth, dim) ##########\n",
    "# acc_test_all : Tensor (width, depth, dim)\n",
    "# num_param_all: Tensor (width, depth)\n",
    "# acc_solved_all:  Tensor (width, depth)\n",
    "# dim_solved_all:  Tensor (width, depth)\n",
    "############################################################\n",
    "nw, nd, nn= len(width), len(depth), len(dim)\n",
    "\n",
    "acc_test_all  = np.zeros((len(width), len(depth), len(dim)))\n",
    "num_param_all = np.zeros((len(width), len(depth)))\n",
    "acc_solved_all = np.zeros((len(width), len(depth)))\n",
    "dim_solved_all = np.zeros((len(width), len(depth)))\n",
    "\n",
    "mode = 3         # {0: test loss, 1: test acc}\n",
    "error_files = [] #  record the error file\n",
    "\n",
    "# 2.1 construct acc_test_all and num_param_all\n",
    "for id_w in range(len(width)):\n",
    "    w = width[id_w]\n",
    "    for id_ll in range(len(depth)):\n",
    "        ll = depth[id_ll]\n",
    "        for id_d in range(len(dim)):\n",
    "            d = dim[id_d]\n",
    "            \n",
    "            # 2.1.1 Read the results, \n",
    "            for f in diary_names:\n",
    "                if '_'+str(d)+'_'+str(ll)+'_'+str(w)+'/' in f:\n",
    "                    # print \"%d is in\" % d + f\n",
    "                    \n",
    "                    with open(f,'r') as ff:\n",
    "                        lines0 = ff.readlines()\n",
    "                        try:\n",
    "                            R = extract_num(lines0)\n",
    "                            R = np.array(R)\n",
    "\n",
    "                        except ValueError:\n",
    "                            error_files.append((w,ll,d))\n",
    "                            R = np.zeros(len(R))\n",
    "                            print \"Error. Can not read config: depth %d, width %d and dim %d.\" % (ll, w, d) \n",
    "                            # break\n",
    "                            \n",
    "                            \n",
    "            # 2.1.2 Assign the results\n",
    "            r = R[mode]  \n",
    "            acc_test_all[id_w,id_ll,id_d]=r\n",
    "            if d==0:\n",
    "                num_param_all[id_w,id_ll]=parse_num_params(lines0) \n",
    "                \n",
    "# 2.2 construct acc_solved_all and dim_solved_all           \n",
    "for id_w in range(len(width)):\n",
    "    w = width[id_w]\n",
    "    for id_ll in range(len(depth)):\n",
    "        ll = depth[id_ll]\n",
    "        for id_d in range(len(dim)):\n",
    "            d = dim[id_d]\n",
    "            \n",
    "            r = acc_test_all[id_w,id_ll,id_d]\n",
    "            if d==0:\n",
    "                test_acc_bl = r # 1.0 # r        \n",
    "                # print \"Acc goal is: \" + str(test_acc_sl) + \" for network with depth \" + str(ll) + \" width \"+ str(w)\n",
    "            else:\n",
    "                test_acc = r\n",
    "                if test_acc>test_acc_bl*0.9:\n",
    "                    acc_solved_all[id_w,id_ll]=test_acc\n",
    "                    dim_solved_all[id_w,id_ll]=d\n",
    "                    # print \"Intrinsic dim is: \" + str(d) + \" for network with depth \" + str(ll) + \" width \"+ str(w)\n",
    "                    # print \"\\n\"\n",
    "                    break\n",
    "                    \n",
    "\n",
    "########## 3. Construct Tensors for Analysis (width, depth, dim) ##########                    \n",
    "acc_base  = acc_test_all[:,:,0]\n",
    "acc_solve = acc_base*0.9\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline results\n",
      "[[[ 0.6174   0.11322  0.17218  0.24892  0.32556  0.39744  0.46654  0.53872\n",
      "    0.60514  0.66908  0.72652  0.78054  0.81432  0.85036  0.87028  0.88712\n",
      "    0.90152  0.91366  0.91762  0.92156  0.92516  0.93036  0.9297   0.93134\n",
      "    0.93092  0.93148  0.9277   0.93102  0.93694  0.93462  0.9339   0.93198]]]\n",
      "# Parmas\n",
      "[[ 478410.]]\n",
      "Cross-line results\n",
      "[[ 0.60514]]\n",
      "Cross-line Dim\n",
      "[[ 70000.]]\n",
      "140000\n",
      "[[ 0.93198]]\n"
     ]
    }
   ],
   "source": [
    "print \"Baseline results\"\n",
    "print acc_test_all[:,:,:]\n",
    "\n",
    "print \"# Parmas\"\n",
    "print num_param_all\n",
    "\n",
    "print \"Cross-line results\"\n",
    "print acc_solved_all\n",
    "\n",
    "print \"Cross-line Dim\"\n",
    "print dim_solved_all\n",
    "\n",
    "print dim[15]\n",
    "print acc_test_all[:,:,-1]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### List the config of depth and width, which yields errors in training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in the following configs: width, depth and dim\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "Shape of accuracy tensor: (1, 1, 32)\n",
      "[ 0.11356  0.1169   0.22276  0.32912  0.42362  0.50796  0.59456  0.68228\n",
      "  0.74778  0.7934   0.8173   0.84238  0.847    0.85462  0.8422   0.1092\n",
      "  0.10796  0.10698  0.10658  0.10546  0.10534  0.1054   0.10634  0.10576\n",
      "  0.10468  0.10436  0.10384  0.10286  0.10264  0.10276  0.1032   0.10328]\n"
     ]
    }
   ],
   "source": [
    "E_width, E_depth, E_dim = [],[],[]\n",
    "for item in error_files:\n",
    "    E_width.append(item[0]) \n",
    "    E_depth.append(item[1])\n",
    "    E_dim.append(item[2])\n",
    "\n",
    "str_E_width = \"\".join(str(E_width)).replace(',', '')\n",
    "str_E_depth = \"\".join(str(E_depth)).replace(',', '')\n",
    "str_E_dim   = \"\".join(str(E_dim)).replace(',', '')\n",
    "\n",
    "print \"Error in the following configs: width, depth and dim\"\n",
    "print str_E_width\n",
    "print str_E_depth\n",
    "print str_E_dim\n",
    "\n",
    "print \"Shape of accuracy tensor: \" + str(acc_test_all.shape)\n",
    "\n",
    "print str(acc_test_all[0,0,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the accuracy of specific depth and width, along different dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def check_cfg_results (depth, width, lines0): \n",
    "    for d in dim:\n",
    "        # 1. read the results\n",
    "        for f in diary_names:\n",
    "            if '_'+str(d)+'_'+str(depth)+'_'+str(width)+'/' in f:\n",
    "                # print \"%d is in\" % d + f\n",
    "                diary_names_ordered.append(f)\n",
    "                with open(f,'r') as ff:\n",
    "                    lines0 = ff.readlines()\n",
    "                    try:\n",
    "                        # print lines0\n",
    "                        param_num = parse_num_params(lines0)\n",
    "                        R = extract_num(lines0)\n",
    "                        print R[1]\n",
    "\n",
    "                    except ValueError:\n",
    "                        print \"Error: Can not read\"\n",
    "                        break                                               "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reshape the tensor to 1D for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[400]\n",
      "[2]\n",
      "[[ 478410.]]\n",
      "[[ 70000.]]\n",
      "[ 478410.]\n",
      "[ 70000.]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig_width = width*len(depth)\n",
    "fig_depth = list(itertools.chain.from_iterable(itertools.repeat(x, len(width)) for x in depth))\n",
    "\n",
    "print fig_width\n",
    "print fig_depth\n",
    "print num_param_all\n",
    "print dim_solved_all\n",
    "fig_params_1d = num_param_all.reshape(len(depth)*len(width),order='F')\n",
    "dim_solved_all_1d = dim_solved_all.reshape(len(depth)*len(width),order='F')\n",
    "acc_solved_all_1d = acc_solved_all.reshape(len(depth)*len(width),order='F')\n",
    "print fig_params_1d\n",
    "print dim_solved_all_1d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing Accuracy wrt. Width, Depth and Dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f98a10278d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJcAAAFECAYAAACam9/OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f98a0ffef10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as font_manager\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(20,5.0))\n",
    "\n",
    "for i in range(acc_test_all.shape[2]):\n",
    "    acc = acc_test_all[:,:,i].reshape(len(depth)*len(width),order='F')\n",
    "    if i==0:\n",
    "        plt.scatter(fig_params_1d, acc, s=(np.array(fig_width)**1.8)/100, c=fig_depth, edgecolors='k') \n",
    "        plt.scatter(fig_params_1d, 0.9*acc, marker=\"_\", s=300, c='k', edgecolors='r') \n",
    "    else:\n",
    "        plt.scatter(fig_params_1d, acc, s=(np.array(fig_width)**1.8)/100, c=fig_depth, facecolors='None', linewidth=np.array(dim[i])/300.0) \n",
    "\n",
    "        \n",
    "ax = plt.gca()\n",
    "plt.colorbar(label=\"Depth\")\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.grid(True)\n",
    "\n",
    "ax.set_ylim(0.8, 1.0)\n",
    "ax.set_xlim(0.3E4, 1.5E6)\n",
    "\n",
    "plt.xlabel('# parameters')\n",
    "plt.ylabel('# accuracy')\n",
    "\n",
    "#make a legend:\n",
    "pws = width\n",
    "for pw in pws:\n",
    "    plt.scatter([], [], s=(pw**1.8)/1000, c=\"k\",label=str(pw))\n",
    "\n",
    "h, l = plt.gca().get_legend_handles_labels()\n",
    "plt.legend(h[0:], l[0:], labelspacing=1.2, title=\"layer width\", borderpad=1, loc='best', bbox_to_anchor=(1.25, 1),\n",
    "             frameon=True, framealpha=0.6, edgecolor=\"k\", facecolor=\"w\")\n",
    "\n",
    " \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Accuracy of Intrinsic dim for #parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f989cb48310>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAFQCAYAAAACzLcCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nImKSJ0mSJEmLyP8DShhhFIrxGbEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f989cb1ac90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,5))\n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 16}\n",
    "\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "plt.scatter(fig_params_1d, acc_solved_all_1d, s=(np.array(fig_width)**2.0)/100, c=fig_depth, edgecolors='k') \n",
    "\n",
    "ax = plt.gca()\n",
    "plt.colorbar(label=\"Depth\")\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.grid(True)\n",
    "\n",
    "ax.set_ylim(0.0, 1.0)\n",
    "ax.set_xlim(0.3E5, 1.5E6)\n",
    "\n",
    "\n",
    "plt.xlabel('# parameters')\n",
    "plt.ylabel('# accuracy')\n",
    "\n",
    "#make a legend:\n",
    "pws = width\n",
    "for pw in pws:\n",
    "    plt.scatter([], [], s=(pw**1.8)/100, c=\"k\",label=str(pw))\n",
    "\n",
    "h, l = plt.gca().get_legend_handles_labels()\n",
    "plt.legend(h[0:], l[0:], labelspacing=1.2, title=\"layer width\", borderpad=1, \n",
    "            frameon=True, framealpha=0.6, edgecolor=\"k\", facecolor=\"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Intrinsic dim for #parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA34AAAFQCAYAAADgEKx7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f98977758d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,5))\n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 16}\n",
    "\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "plt.scatter(fig_params_1d, dim_solved_all_1d, s=(np.array(fig_width)**2.0)/100, c=fig_depth, edgecolors='k') \n",
    "\n",
    "ax = plt.gca()\n",
    "plt.colorbar(label=\"Depth\")\n",
    "\n",
    "ax.set_xscale('log')\n",
    "\n",
    "ax.grid(True)\n",
    "ax.set_ylim(0, 400)\n",
    "\n",
    "plt.xlabel('# model parameters')\n",
    "plt.ylabel('# intrinsic dimension')\n",
    "ax.set_xlim(0.3E5, 1.5E6)\n",
    "\n",
    "#make a legend:\n",
    "pws = width\n",
    "for pw in pws:\n",
    "    plt.scatter([], [], s=(pw**1.8)/100, c=\"k\",label=str(pw))\n",
    "\n",
    "h, l = plt.gca().get_legend_handles_labels()\n",
    "plt.legend(h[0:], l[0:], labelspacing=1.2, title=\"layer width\", borderpad=1, \n",
    "            frameon=True, framealpha=0.6, edgecolor=\"k\", facecolor=\"w\",loc='best')\n",
    "\n",
    "fig.savefig(\"fnn_dim_global_nl.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance comparison with Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e26c06f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(35,25))\n",
    "fig.subplots_adjust(hspace=0.3)\n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 16}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "for i in range(nw):\n",
    "    for j in range(nd):\n",
    "        if j>0:\n",
    "            id = i*nd+j+1\n",
    "            ax = plt.subplot(nw, nd, id)\n",
    "\n",
    "            plt.scatter(dim, acc_test_all[i,j,:], edgecolor=\"k\", facecolor=\"w\", s=60 )\n",
    "            ax.plot(dim, acc_test_all[i,j,0]*np.ones(nn)*0.9,'r-.', label=\"Testing: baseline\")\n",
    "            ax.set_xlabel('Intrinsic Dim')\n",
    "            ax.set_ylabel('Accuracy')\n",
    "            ax.set_title('width %d, depth %d' %(width[i], depth[j]))\n",
    "            plt.grid()\n",
    "            ax.set_ylim([-0.1,1.1])\n",
    "\n",
    "fig.savefig(\"fnn_all_configs_nl.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAFKCAYAAADlv9PgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e246e6f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i,j=0,0\n",
    "\n",
    "fig, ax = subplots(figsize=(6,5) )\n",
    "  \n",
    "font = {'size'   : 12}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "\n",
    "plot(dim[1:], acc_test_all[0,0,1:], 'o', mec='b', mfc=(.8,.8,1), ms=10)\n",
    "plot(dim_solved_all[i,j], acc_solved_all[i,j], 'o', mec='b', mfc='b', ms=10)\n",
    "axhline(acc_test_all[0,0,0], ls='-', color='k',label='baseline')\n",
    "axhline(acc_test_all[0,0,0] * .9, ls=':', color='k',label='solved')\n",
    "plt.legend()\n",
    "ax.set_xlabel('Subspace dimension $d$')\n",
    "ax.set_ylabel('Validation accuracy')\n",
    "\n",
    "# ax.set_title('width %d, depth %d' %(width[i], depth[j]))\n",
    "plt.grid()\n",
    "ax.set_ylim([0.0,0.95])\n",
    "        \n",
    "fig.savefig(\"figs/fnn_mnistPL_W\"+str(width[i])+\"_L\"+str(depth[j])+\".pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.61876,  0.11224,  0.16344,  0.21748,  0.26432,  0.3095 ,\n",
       "          0.35228,  0.39068,  0.42614,  0.45558,  0.48398,  0.5137 ,\n",
       "          0.53654,  0.55952,  0.57662,  0.59224,  0.60614,  0.61776,\n",
       "          0.62912,  0.63658,  0.64432,  0.64564,  0.64928,  0.65652,\n",
       "          0.6527 ,  0.65   ,  0.62032,  0.10548,  0.10428,  0.1036 ,\n",
       "          0.10502,  0.10298]]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_test_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/users/chunyuan.li/public_results/chun/171019_004350_5393fd2_lrb_fnn_mnist_t500_pl_10000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_044334_5393fd2_lrb_fnn_mnist_t500_pl_160000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_055040_5393fd2_lrb_fnn_mnist_t500_pl_210000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_044401_5393fd2_lrb_fnn_mnist_t500_pl_180000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_013656_5393fd2_lrb_fnn_mnist_t500_pl_150000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_020157_5393fd2_lrb_fnn_mnist_t500_pl_150000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234551_5393fd2_lrb_fnn_mnist_t500_pl_290000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234410_5393fd2_lrb_fnn_mnist_t500_pl_150000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034518_5393fd2_lrb_fnn_mnist_t500_pl_60000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_044138_5393fd2_lrb_fnn_mnist_t500_pl_90000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_050743_5393fd2_lrb_fnn_mnist_t500_pl_210000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234606_5393fd2_lrb_fnn_mnist_t500_pl_40000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_005000_5393fd2_lrb_fnn_mnist_t500_pl_290000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_020129_5393fd2_lrb_fnn_mnist_t500_pl_90000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_014334_5393fd2_lrb_fnn_mnist_t500_pl_190000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_055536_5393fd2_lrb_fnn_mnist_t500_pl_300000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_014257_5393fd2_lrb_fnn_mnist_t500_pl_150000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004918_5393fd2_lrb_fnn_mnist_t500_pl_140000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_020141_5393fd2_lrb_fnn_mnist_t500_pl_110000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_014159_5393fd2_lrb_fnn_mnist_t500_pl_110000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034313_5393fd2_lrb_fnn_mnist_t500_pl_120000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_043930_5393fd2_lrb_fnn_mnist_t500_pl_70000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_052659_5393fd2_lrb_fnn_mnist_t500_pl_230000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024955_5393fd2_lrb_fnn_mnist_t500_pl_290000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_055603_5393fd2_lrb_fnn_mnist_t500_pl_1000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004304_5393fd2_lrb_fnn_mnist_t500_pl_280000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_014056_5393fd2_lrb_fnn_mnist_t500_pl_80000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_025020_5393fd2_lrb_fnn_mnist_t500_pl_10000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_024235_5393fd2_lrb_fnn_mnist_t500_pl_60000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234416_5393fd2_lrb_fnn_mnist_t500_pl_180000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004601_5393fd2_lrb_fnn_mnist_t500_pl_270000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004929_5393fd2_lrb_fnn_mnist_t500_pl_170000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_051226_5393fd2_lrb_fnn_mnist_t500_pl_70000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234501_5393fd2_lrb_fnn_mnist_t500_pl_260000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_062018_5393fd2_lrb_fnn_mnist_t500_pl_210000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_020304_5393fd2_lrb_fnn_mnist_t500_pl_20000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234341_5393fd2_lrb_fnn_mnist_t500_pl_1000_1_50_1/diary', '/home/users/chunyuan.li/public_results/chun/171019_044524_5393fd2_lrb_fnn_mnist_t500_pl_240000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_040215_5393fd2_lrb_fnn_mnist_t500_pl_160000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234458_5393fd2_lrb_fnn_mnist_t500_pl_240000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_043644_5393fd2_lrb_fnn_mnist_t500_pl_30000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_023752_5393fd2_lrb_fnn_mnist_t500_pl_80000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_054836_5393fd2_lrb_fnn_mnist_t500_pl_130000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_020100_5393fd2_lrb_fnn_mnist_t500_pl_70000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004340_5393fd2_lrb_fnn_mnist_t500_pl_1000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_015623_5393fd2_lrb_fnn_mnist_t500_pl_20000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234518_5393fd2_lrb_fnn_mnist_t500_pl_140000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_015438_5393fd2_lrb_fnn_mnist_t500_pl_10000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_014753_5393fd2_lrb_fnn_mnist_t500_pl_280000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_020144_5393fd2_lrb_fnn_mnist_t500_pl_130000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_020042_5393fd2_lrb_fnn_mnist_t500_pl_50000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_050711_5393fd2_lrb_fnn_mnist_t500_pl_140000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_060715_5393fd2_lrb_fnn_mnist_t500_pl_110000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_035801_5393fd2_lrb_fnn_mnist_t500_pl_90000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_023641_5393fd2_lrb_fnn_mnist_t500_pl_60000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004254_5393fd2_lrb_fnn_mnist_t500_pl_270000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_055126_5393fd2_lrb_fnn_mnist_t500_pl_230000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_004459_5393fd2_lrb_fnn_mnist_t500_pl_80000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_024411_5393fd2_lrb_fnn_mnist_t500_pl_110000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234503_5393fd2_lrb_fnn_mnist_t500_pl_300000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_052139_5393fd2_lrb_fnn_mnist_t500_pl_110000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_050702_5393fd2_lrb_fnn_mnist_t500_pl_120000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_050630_5393fd2_lrb_fnn_mnist_t500_pl_70000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234447_5393fd2_lrb_fnn_mnist_t500_pl_140000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_052859_5393fd2_lrb_fnn_mnist_t500_pl_300000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_035623_5393fd2_lrb_fnn_mnist_t500_pl_60000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_235445_5393fd2_lrb_fnn_mnist_t500_pl_100000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_053719_5393fd2_lrb_fnn_mnist_t500_pl_30000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_032925_5393fd2_lrb_fnn_mnist_t500_pl_240000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234606_5393fd2_lrb_fnn_mnist_t500_pl_30000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_044214_5393fd2_lrb_fnn_mnist_t500_pl_120000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_013844_5393fd2_lrb_fnn_mnist_t500_pl_260000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234516_5393fd2_lrb_fnn_mnist_t500_pl_100000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234518_5393fd2_lrb_fnn_mnist_t500_pl_130000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034556_5393fd2_lrb_fnn_mnist_t500_pl_80000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_040124_5393fd2_lrb_fnn_mnist_t500_pl_140000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_004500_5393fd2_lrb_fnn_mnist_t500_pl_90000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_010051_5393fd2_lrb_fnn_mnist_t500_pl_130000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_025954_5393fd2_lrb_fnn_mnist_t500_pl_120000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_014130_5393fd2_lrb_fnn_mnist_t500_pl_90000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_040632_5393fd2_lrb_fnn_mnist_t500_pl_180000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_004954_5393fd2_lrb_fnn_mnist_t500_pl_260000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234355_5393fd2_lrb_fnn_mnist_t500_pl_70000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034908_5393fd2_lrb_fnn_mnist_t500_pl_190000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234537_5393fd2_lrb_fnn_mnist_t500_pl_230000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034855_5393fd2_lrb_fnn_mnist_t500_pl_150000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004626_5393fd2_lrb_fnn_mnist_t500_pl_60000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024149_5393fd2_lrb_fnn_mnist_t500_pl_40000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_040712_5393fd2_lrb_fnn_mnist_t500_pl_190000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_235504_5393fd2_lrb_fnn_mnist_t500_pl_110000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_052609_5393fd2_lrb_fnn_mnist_t500_pl_190000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234402_5393fd2_lrb_fnn_mnist_t500_pl_110000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004528_5393fd2_lrb_fnn_mnist_t500_pl_150000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234527_5393fd2_lrb_fnn_mnist_t500_pl_200000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_052618_5393fd2_lrb_fnn_mnist_t500_pl_200000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_014307_5393fd2_lrb_fnn_mnist_t500_pl_170000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_020203_5393fd2_lrb_fnn_mnist_t500_pl_170000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_040102_5393fd2_lrb_fnn_mnist_t500_pl_130000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_044442_5393fd2_lrb_fnn_mnist_t500_pl_230000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_013852_5393fd2_lrb_fnn_mnist_t500_pl_270000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_004601_5393fd2_lrb_fnn_mnist_t500_pl_260000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034432_5393fd2_lrb_fnn_mnist_t500_pl_280000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_013840_5393fd2_lrb_fnn_mnist_t500_pl_230000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_055008_5393fd2_lrb_fnn_mnist_t500_pl_170000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234514_5393fd2_lrb_fnn_mnist_t500_pl_90000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034445_5393fd2_lrb_fnn_mnist_t500_pl_20000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_014145_5393fd2_lrb_fnn_mnist_t500_pl_100000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_024422_5393fd2_lrb_fnn_mnist_t500_pl_120000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234433_5393fd2_lrb_fnn_mnist_t500_pl_70000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_013840_5393fd2_lrb_fnn_mnist_t500_pl_240000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_013817_5393fd2_lrb_fnn_mnist_t500_pl_200000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_005000_5393fd2_lrb_fnn_mnist_t500_pl_300000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034438_5393fd2_lrb_fnn_mnist_t500_pl_1000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_055221_5393fd2_lrb_fnn_mnist_t500_pl_260000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_055545_5393fd2_lrb_fnn_mnist_t500_pl_0_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234442_5393fd2_lrb_fnn_mnist_t500_pl_110000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_055233_5393fd2_lrb_fnn_mnist_t500_pl_270000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_004946_5393fd2_lrb_fnn_mnist_t500_pl_220000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_050759_5393fd2_lrb_fnn_mnist_t500_pl_240000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_055921_5393fd2_lrb_fnn_mnist_t500_pl_20000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_020142_5393fd2_lrb_fnn_mnist_t500_pl_120000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_024813_5393fd2_lrb_fnn_mnist_t500_pl_210000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234449_5393fd2_lrb_fnn_mnist_t500_pl_180000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034327_5393fd2_lrb_fnn_mnist_t500_pl_140000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004453_5393fd2_lrb_fnn_mnist_t500_pl_60000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234442_5393fd2_lrb_fnn_mnist_t500_pl_130000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_045317_5393fd2_lrb_fnn_mnist_t500_pl_270000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_020240_5393fd2_lrb_fnn_mnist_t500_pl_280000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_033348_5393fd2_lrb_fnn_mnist_t500_pl_250000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_051422_5393fd2_lrb_fnn_mnist_t500_pl_100000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_013833_5393fd2_lrb_fnn_mnist_t500_pl_220000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_033506_5393fd2_lrb_fnn_mnist_t500_pl_260000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_020300_5393fd2_lrb_fnn_mnist_t500_pl_1000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004355_5393fd2_lrb_fnn_mnist_t500_pl_20000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_024050_5393fd2_lrb_fnn_mnist_t500_pl_250000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_043214_5393fd2_lrb_fnn_mnist_t500_pl_0_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234553_5393fd2_lrb_fnn_mnist_t500_pl_300000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_044301_5393fd2_lrb_fnn_mnist_t500_pl_140000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_045541_5393fd2_lrb_fnn_mnist_t500_pl_300000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_044314_5393fd2_lrb_fnn_mnist_t500_pl_150000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_024358_5393fd2_lrb_fnn_mnist_t500_pl_100000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_025006_5393fd2_lrb_fnn_mnist_t500_pl_1000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_024833_5393fd2_lrb_fnn_mnist_t500_pl_220000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_024042_5393fd2_lrb_fnn_mnist_t500_pl_240000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_035057_5393fd2_lrb_fnn_mnist_t500_pl_40000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_023936_5393fd2_lrb_fnn_mnist_t500_pl_130000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004001_5393fd2_lrb_fnn_mnist_t500_pl_220000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_014544_5393fd2_lrb_fnn_mnist_t500_pl_250000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004537_5393fd2_lrb_fnn_mnist_t500_pl_190000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004604_5393fd2_lrb_fnn_mnist_t500_pl_300000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004945_5393fd2_lrb_fnn_mnist_t500_pl_210000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004558_5393fd2_lrb_fnn_mnist_t500_pl_250000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_032327_5393fd2_lrb_fnn_mnist_t500_pl_190000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_024039_5393fd2_lrb_fnn_mnist_t500_pl_230000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_014711_5393fd2_lrb_fnn_mnist_t500_pl_260000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004413_5393fd2_lrb_fnn_mnist_t500_pl_30000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_024941_5393fd2_lrb_fnn_mnist_t500_pl_260000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_025122_5393fd2_lrb_fnn_mnist_t500_pl_30000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_025028_5393fd2_lrb_fnn_mnist_t500_pl_20000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004316_5393fd2_lrb_fnn_mnist_t500_pl_300000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_064349_5393fd2_lrb_fnn_mnist_t500_pl_250000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_061315_5393fd2_lrb_fnn_mnist_t500_pl_190000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004516_5393fd2_lrb_fnn_mnist_t500_pl_130000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234604_5393fd2_lrb_fnn_mnist_t500_pl_20000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_054326_5393fd2_lrb_fnn_mnist_t500_pl_70000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234417_5393fd2_lrb_fnn_mnist_t500_pl_200000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_050803_5393fd2_lrb_fnn_mnist_t500_pl_250000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_045502_5393fd2_lrb_fnn_mnist_t500_pl_290000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_024021_5393fd2_lrb_fnn_mnist_t500_pl_200000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234506_5393fd2_lrb_fnn_mnist_t500_pl_30000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_050727_5393fd2_lrb_fnn_mnist_t500_pl_190000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_053319_5393fd2_lrb_fnn_mnist_t500_pl_1000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_055912_5393fd2_lrb_fnn_mnist_t500_pl_10000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_013902_5393fd2_lrb_fnn_mnist_t500_pl_1000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_235250_5393fd2_lrb_fnn_mnist_t500_pl_90000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_003840_5393fd2_lrb_fnn_mnist_t500_pl_200000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004533_5393fd2_lrb_fnn_mnist_t500_pl_170000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_023615_5393fd2_lrb_fnn_mnist_t500_pl_50000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024423_5393fd2_lrb_fnn_mnist_t500_pl_130000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034415_5393fd2_lrb_fnn_mnist_t500_pl_250000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034246_5393fd2_lrb_fnn_mnist_t500_pl_70000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_005006_5393fd2_lrb_fnn_mnist_t500_pl_20000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_235621_5393fd2_lrb_fnn_mnist_t500_pl_120000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_060353_5393fd2_lrb_fnn_mnist_t500_pl_80000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_013917_5393fd2_lrb_fnn_mnist_t500_pl_30000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_053744_5393fd2_lrb_fnn_mnist_t500_pl_50000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234557_5393fd2_lrb_fnn_mnist_t500_pl_0_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_033532_5393fd2_lrb_fnn_mnist_t500_pl_270000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_050413_5393fd2_lrb_fnn_mnist_t500_pl_30000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234434_5393fd2_lrb_fnn_mnist_t500_pl_80000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004200_5393fd2_lrb_fnn_mnist_t500_pl_260000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004029_5393fd2_lrb_fnn_mnist_t500_pl_230000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034320_5393fd2_lrb_fnn_mnist_t500_pl_130000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_052835_5393fd2_lrb_fnn_mnist_t500_pl_280000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_052845_5393fd2_lrb_fnn_mnist_t500_pl_290000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034413_5393fd2_lrb_fnn_mnist_t500_pl_230000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_050842_5393fd2_lrb_fnn_mnist_t500_pl_30000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_025300_5393fd2_lrb_fnn_mnist_t500_pl_70000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_003929_5393fd2_lrb_fnn_mnist_t500_pl_210000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034225_5393fd2_lrb_fnn_mnist_t500_pl_50000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234416_5393fd2_lrb_fnn_mnist_t500_pl_170000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_060917_5393fd2_lrb_fnn_mnist_t500_pl_130000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004615_5393fd2_lrb_fnn_mnist_t500_pl_10000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024135_5393fd2_lrb_fnn_mnist_t500_pl_20000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034514_5393fd2_lrb_fnn_mnist_t500_pl_50000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_050040_5393fd2_lrb_fnn_mnist_t500_pl_1000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_041832_5393fd2_lrb_fnn_mnist_t500_pl_220000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234344_5393fd2_lrb_fnn_mnist_t500_pl_10000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034303_5393fd2_lrb_fnn_mnist_t500_pl_90000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_060348_5393fd2_lrb_fnn_mnist_t500_pl_70000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034413_5393fd2_lrb_fnn_mnist_t500_pl_240000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004906_5393fd2_lrb_fnn_mnist_t500_pl_120000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234526_5393fd2_lrb_fnn_mnist_t500_pl_190000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_065104_5393fd2_lrb_fnn_mnist_t500_pl_300000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_015220_5393fd2_lrb_fnn_mnist_t500_pl_1000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004511_5393fd2_lrb_fnn_mnist_t500_pl_110000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234409_5393fd2_lrb_fnn_mnist_t500_pl_120000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_024050_5393fd2_lrb_fnn_mnist_t500_pl_260000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_003547_5393fd2_lrb_fnn_mnist_t500_pl_130000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_005017_5393fd2_lrb_fnn_mnist_t500_pl_60000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034831_5393fd2_lrb_fnn_mnist_t500_pl_110000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_013843_5393fd2_lrb_fnn_mnist_t500_pl_250000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034833_5393fd2_lrb_fnn_mnist_t500_pl_120000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024946_5393fd2_lrb_fnn_mnist_t500_pl_270000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_023953_5393fd2_lrb_fnn_mnist_t500_pl_140000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004556_5393fd2_lrb_fnn_mnist_t500_pl_240000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_050841_5393fd2_lrb_fnn_mnist_t500_pl_20000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_050752_5393fd2_lrb_fnn_mnist_t500_pl_230000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034423_5393fd2_lrb_fnn_mnist_t500_pl_260000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_052444_5393fd2_lrb_fnn_mnist_t500_pl_130000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_020220_5393fd2_lrb_fnn_mnist_t500_pl_210000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034853_5393fd2_lrb_fnn_mnist_t500_pl_140000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034136_5393fd2_lrb_fnn_mnist_t500_pl_10000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_050656_5393fd2_lrb_fnn_mnist_t500_pl_110000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034934_5393fd2_lrb_fnn_mnist_t500_pl_280000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_045321_5393fd2_lrb_fnn_mnist_t500_pl_280000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004953_5393fd2_lrb_fnn_mnist_t500_pl_250000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234609_5393fd2_lrb_fnn_mnist_t500_pl_60000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_043219_5393fd2_lrb_fnn_mnist_t500_pl_1000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_053733_5393fd2_lrb_fnn_mnist_t500_pl_40000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_050814_5393fd2_lrb_fnn_mnist_t500_pl_270000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_050823_5393fd2_lrb_fnn_mnist_t500_pl_300000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_005429_5393fd2_lrb_fnn_mnist_t500_pl_70000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_003817_5393fd2_lrb_fnn_mnist_t500_pl_170000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_033852_5393fd2_lrb_fnn_mnist_t500_pl_300000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_025336_5393fd2_lrb_fnn_mnist_t500_pl_100000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_061137_5393fd2_lrb_fnn_mnist_t500_pl_150000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_052910_5393fd2_lrb_fnn_mnist_t500_pl_0_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234603_5393fd2_lrb_fnn_mnist_t500_pl_10000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034337_5393fd2_lrb_fnn_mnist_t500_pl_170000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_005008_5393fd2_lrb_fnn_mnist_t500_pl_30000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_013859_5393fd2_lrb_fnn_mnist_t500_pl_0_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234504_5393fd2_lrb_fnn_mnist_t500_pl_0_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_024104_5393fd2_lrb_fnn_mnist_t500_pl_280000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004520_5393fd2_lrb_fnn_mnist_t500_pl_140000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_054811_5393fd2_lrb_fnn_mnist_t500_pl_120000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_014542_5393fd2_lrb_fnn_mnist_t500_pl_240000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_052522_5393fd2_lrb_fnn_mnist_t500_pl_160000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_050029_5393fd2_lrb_fnn_mnist_t500_pl_0_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_013924_5393fd2_lrb_fnn_mnist_t500_pl_40000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_043220_5393fd2_lrb_fnn_mnist_t500_pl_10000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034210_5393fd2_lrb_fnn_mnist_t500_pl_30000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_055929_5393fd2_lrb_fnn_mnist_t500_pl_30000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_035555_5393fd2_lrb_fnn_mnist_t500_pl_50000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034350_5393fd2_lrb_fnn_mnist_t500_pl_200000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234447_5393fd2_lrb_fnn_mnist_t500_pl_150000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_051402_5393fd2_lrb_fnn_mnist_t500_pl_90000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_015019_5393fd2_lrb_fnn_mnist_t500_pl_300000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_024115_5393fd2_lrb_fnn_mnist_t500_pl_0_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_050716_5393fd2_lrb_fnn_mnist_t500_pl_150000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004040_5393fd2_lrb_fnn_mnist_t500_pl_240000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034954_5393fd2_lrb_fnn_mnist_t500_pl_30000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_063938_5393fd2_lrb_fnn_mnist_t500_pl_230000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004539_5393fd2_lrb_fnn_mnist_t500_pl_200000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234620_5393fd2_lrb_fnn_mnist_t500_pl_80000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_040019_5393fd2_lrb_fnn_mnist_t500_pl_110000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_004925_5393fd2_lrb_fnn_mnist_t500_pl_160000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234400_5393fd2_lrb_fnn_mnist_t500_pl_100000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234521_5393fd2_lrb_fnn_mnist_t500_pl_150000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034504_5393fd2_lrb_fnn_mnist_t500_pl_40000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034926_5393fd2_lrb_fnn_mnist_t500_pl_240000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034339_5393fd2_lrb_fnn_mnist_t500_pl_180000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034928_5393fd2_lrb_fnn_mnist_t500_pl_270000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034150_5393fd2_lrb_fnn_mnist_t500_pl_20000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_040336_5393fd2_lrb_fnn_mnist_t500_pl_170000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234442_5393fd2_lrb_fnn_mnist_t500_pl_120000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_025129_5393fd2_lrb_fnn_mnist_t500_pl_40000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_014222_5393fd2_lrb_fnn_mnist_t500_pl_120000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_020230_5393fd2_lrb_fnn_mnist_t500_pl_250000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_023906_5393fd2_lrb_fnn_mnist_t500_pl_110000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004938_5393fd2_lrb_fnn_mnist_t500_pl_180000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_013859_5393fd2_lrb_fnn_mnist_t500_pl_300000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_061151_5393fd2_lrb_fnn_mnist_t500_pl_170000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_024119_5393fd2_lrb_fnn_mnist_t500_pl_1000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_061024_5393fd2_lrb_fnn_mnist_t500_pl_140000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234456_5393fd2_lrb_fnn_mnist_t500_pl_220000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234347_5393fd2_lrb_fnn_mnist_t500_pl_30000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_064420_5393fd2_lrb_fnn_mnist_t500_pl_260000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_052424_5393fd2_lrb_fnn_mnist_t500_pl_120000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_032402_5393fd2_lrb_fnn_mnist_t500_pl_210000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234432_5393fd2_lrb_fnn_mnist_t500_pl_40000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034927_5393fd2_lrb_fnn_mnist_t500_pl_250000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_060724_5393fd2_lrb_fnn_mnist_t500_pl_120000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_014234_5393fd2_lrb_fnn_mnist_t500_pl_130000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_003829_5393fd2_lrb_fnn_mnist_t500_pl_190000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_050651_5393fd2_lrb_fnn_mnist_t500_pl_90000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_040158_5393fd2_lrb_fnn_mnist_t500_pl_150000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_005822_5393fd2_lrb_fnn_mnist_t500_pl_110000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_015045_5393fd2_lrb_fnn_mnist_t500_pl_0_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_050819_5393fd2_lrb_fnn_mnist_t500_pl_280000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004958_5393fd2_lrb_fnn_mnist_t500_pl_280000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_020250_5393fd2_lrb_fnn_mnist_t500_pl_300000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_024035_5393fd2_lrb_fnn_mnist_t500_pl_220000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034330_5393fd2_lrb_fnn_mnist_t500_pl_150000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_025313_5393fd2_lrb_fnn_mnist_t500_pl_80000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234548_5393fd2_lrb_fnn_mnist_t500_pl_280000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_050303_5393fd2_lrb_fnn_mnist_t500_pl_10000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_003759_5393fd2_lrb_fnn_mnist_t500_pl_150000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234539_5393fd2_lrb_fnn_mnist_t500_pl_250000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_004826_5393fd2_lrb_fnn_mnist_t500_pl_80000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_043059_5393fd2_lrb_fnn_mnist_t500_pl_260000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_053618_5393fd2_lrb_fnn_mnist_t500_pl_10000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_005005_5393fd2_lrb_fnn_mnist_t500_pl_0_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_054347_5393fd2_lrb_fnn_mnist_t500_pl_90000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_050718_5393fd2_lrb_fnn_mnist_t500_pl_170000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_051334_5393fd2_lrb_fnn_mnist_t500_pl_80000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_023854_5393fd2_lrb_fnn_mnist_t500_pl_100000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034431_5393fd2_lrb_fnn_mnist_t500_pl_270000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234502_5393fd2_lrb_fnn_mnist_t500_pl_290000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234440_5393fd2_lrb_fnn_mnist_t500_pl_100000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004530_5393fd2_lrb_fnn_mnist_t500_pl_160000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_050325_5393fd2_lrb_fnn_mnist_t500_pl_20000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004603_5393fd2_lrb_fnn_mnist_t500_pl_290000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034900_5393fd2_lrb_fnn_mnist_t500_pl_160000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024949_5393fd2_lrb_fnn_mnist_t500_pl_280000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_014307_5393fd2_lrb_fnn_mnist_t500_pl_160000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004848_5393fd2_lrb_fnn_mnist_t500_pl_90000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_021423_5393fd2_lrb_fnn_mnist_t500_pl_30000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_005006_5393fd2_lrb_fnn_mnist_t500_pl_10000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034950_5393fd2_lrb_fnn_mnist_t500_pl_10000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_043125_5393fd2_lrb_fnn_mnist_t500_pl_280000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_004944_5393fd2_lrb_fnn_mnist_t500_pl_200000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024440_5393fd2_lrb_fnn_mnist_t500_pl_140000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_050705_5393fd2_lrb_fnn_mnist_t500_pl_130000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_050655_5393fd2_lrb_fnn_mnist_t500_pl_100000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_055050_5393fd2_lrb_fnn_mnist_t500_pl_220000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234345_5393fd2_lrb_fnn_mnist_t500_pl_20000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_013858_5393fd2_lrb_fnn_mnist_t500_pl_290000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_005540_5393fd2_lrb_fnn_mnist_t500_pl_80000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_024921_5393fd2_lrb_fnn_mnist_t500_pl_240000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234529_5393fd2_lrb_fnn_mnist_t500_pl_210000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034912_5393fd2_lrb_fnn_mnist_t500_pl_210000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004422_5393fd2_lrb_fnn_mnist_t500_pl_40000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_043749_5393fd2_lrb_fnn_mnist_t500_pl_50000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_020228_5393fd2_lrb_fnn_mnist_t500_pl_240000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_044427_5393fd2_lrb_fnn_mnist_t500_pl_200000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_044529_5393fd2_lrb_fnn_mnist_t500_pl_250000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034939_5393fd2_lrb_fnn_mnist_t500_pl_300000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234347_5393fd2_lrb_fnn_mnist_t500_pl_40000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_051049_5393fd2_lrb_fnn_mnist_t500_pl_40000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_043547_5393fd2_lrb_fnn_mnist_t500_pl_20000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_013715_5393fd2_lrb_fnn_mnist_t500_pl_190000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_050646_5393fd2_lrb_fnn_mnist_t500_pl_80000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034128_5393fd2_lrb_fnn_mnist_t500_pl_1000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234504_5393fd2_lrb_fnn_mnist_t500_pl_20000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234355_5393fd2_lrb_fnn_mnist_t500_pl_80000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234341_5393fd2_lrb_fnn_mnist_t500_pl_1000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_055452_5393fd2_lrb_fnn_mnist_t500_pl_290000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234533_5393fd2_lrb_fnn_mnist_t500_pl_220000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_004507_5393fd2_lrb_fnn_mnist_t500_pl_100000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_042941_5393fd2_lrb_fnn_mnist_t500_pl_230000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_024110_5393fd2_lrb_fnn_mnist_t500_pl_300000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_014949_5393fd2_lrb_fnn_mnist_t500_pl_290000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004949_5393fd2_lrb_fnn_mnist_t500_pl_230000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234454_5393fd2_lrb_fnn_mnist_t500_pl_200000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_055149_5393fd2_lrb_fnn_mnist_t500_pl_240000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_060310_5393fd2_lrb_fnn_mnist_t500_pl_40000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_044150_5393fd2_lrb_fnn_mnist_t500_pl_100000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234455_5393fd2_lrb_fnn_mnist_t500_pl_210000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234449_5393fd2_lrb_fnn_mnist_t500_pl_160000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024050_5393fd2_lrb_fnn_mnist_t500_pl_270000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234507_5393fd2_lrb_fnn_mnist_t500_pl_40000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_052517_5393fd2_lrb_fnn_mnist_t500_pl_150000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_050840_5393fd2_lrb_fnn_mnist_t500_pl_10000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_005005_5393fd2_lrb_fnn_mnist_t500_pl_1000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_024001_5393fd2_lrb_fnn_mnist_t500_pl_160000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_023916_5393fd2_lrb_fnn_mnist_t500_pl_120000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_054054_5393fd2_lrb_fnn_mnist_t500_pl_60000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_024523_5393fd2_lrb_fnn_mnist_t500_pl_160000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234427_5393fd2_lrb_fnn_mnist_t500_pl_300000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234548_5393fd2_lrb_fnn_mnist_t500_pl_270000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_020218_5393fd2_lrb_fnn_mnist_t500_pl_200000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_013823_5393fd2_lrb_fnn_mnist_t500_pl_210000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_020217_5393fd2_lrb_fnn_mnist_t500_pl_190000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034436_5393fd2_lrb_fnn_mnist_t500_pl_290000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_043058_5393fd2_lrb_fnn_mnist_t500_pl_250000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234421_5393fd2_lrb_fnn_mnist_t500_pl_220000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004621_5393fd2_lrb_fnn_mnist_t500_pl_40000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004940_5393fd2_lrb_fnn_mnist_t500_pl_190000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_052806_5393fd2_lrb_fnn_mnist_t500_pl_260000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_014426_5393fd2_lrb_fnn_mnist_t500_pl_210000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_050804_5393fd2_lrb_fnn_mnist_t500_pl_260000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004515_5393fd2_lrb_fnn_mnist_t500_pl_120000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_040038_5393fd2_lrb_fnn_mnist_t500_pl_120000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_014242_5393fd2_lrb_fnn_mnist_t500_pl_140000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004619_5393fd2_lrb_fnn_mnist_t500_pl_30000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234423_5393fd2_lrb_fnn_mnist_t500_pl_260000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_060326_5393fd2_lrb_fnn_mnist_t500_pl_50000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_050441_5393fd2_lrb_fnn_mnist_t500_pl_40000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_020303_5393fd2_lrb_fnn_mnist_t500_pl_10000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034344_5393fd2_lrb_fnn_mnist_t500_pl_190000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_020113_5393fd2_lrb_fnn_mnist_t500_pl_80000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_020240_5393fd2_lrb_fnn_mnist_t500_pl_270000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_032316_5393fd2_lrb_fnn_mnist_t500_pl_160000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034912_5393fd2_lrb_fnn_mnist_t500_pl_220000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234509_5393fd2_lrb_fnn_mnist_t500_pl_60000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_020157_5393fd2_lrb_fnn_mnist_t500_pl_160000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234607_5393fd2_lrb_fnn_mnist_t500_pl_50000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_064143_5393fd2_lrb_fnn_mnist_t500_pl_240000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_035709_5393fd2_lrb_fnn_mnist_t500_pl_80000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034939_5393fd2_lrb_fnn_mnist_t500_pl_0_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234521_5393fd2_lrb_fnn_mnist_t500_pl_160000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_044158_5393fd2_lrb_fnn_mnist_t500_pl_110000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034905_5393fd2_lrb_fnn_mnist_t500_pl_170000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_054950_5393fd2_lrb_fnn_mnist_t500_pl_160000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034917_5393fd2_lrb_fnn_mnist_t500_pl_230000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234450_5393fd2_lrb_fnn_mnist_t500_pl_190000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_050727_5393fd2_lrb_fnn_mnist_t500_pl_180000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004900_5393fd2_lrb_fnn_mnist_t500_pl_100000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_020251_5393fd2_lrb_fnn_mnist_t500_pl_0_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234353_5393fd2_lrb_fnn_mnist_t500_pl_60000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004615_5393fd2_lrb_fnn_mnist_t500_pl_20000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_064550_5393fd2_lrb_fnn_mnist_t500_pl_280000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_025316_5393fd2_lrb_fnn_mnist_t500_pl_90000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234430_5393fd2_lrb_fnn_mnist_t500_pl_20000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_013932_5393fd2_lrb_fnn_mnist_t500_pl_60000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004453_5393fd2_lrb_fnn_mnist_t500_pl_70000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234507_5393fd2_lrb_fnn_mnist_t500_pl_50000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_015746_5393fd2_lrb_fnn_mnist_t500_pl_40000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_032355_5393fd2_lrb_fnn_mnist_t500_pl_200000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004902_5393fd2_lrb_fnn_mnist_t500_pl_110000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024015_5393fd2_lrb_fnn_mnist_t500_pl_190000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034615_5393fd2_lrb_fnn_mnist_t500_pl_90000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034212_5393fd2_lrb_fnn_mnist_t500_pl_40000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_061359_5393fd2_lrb_fnn_mnist_t500_pl_200000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234558_5393fd2_lrb_fnn_mnist_t500_pl_1000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234431_5393fd2_lrb_fnn_mnist_t500_pl_30000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024304_5393fd2_lrb_fnn_mnist_t500_pl_80000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_032407_5393fd2_lrb_fnn_mnist_t500_pl_230000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234516_5393fd2_lrb_fnn_mnist_t500_pl_110000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234353_5393fd2_lrb_fnn_mnist_t500_pl_50000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_052533_5393fd2_lrb_fnn_mnist_t500_pl_170000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_023725_5393fd2_lrb_fnn_mnist_t500_pl_70000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_060631_5393fd2_lrb_fnn_mnist_t500_pl_100000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234449_5393fd2_lrb_fnn_mnist_t500_pl_170000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234517_5393fd2_lrb_fnn_mnist_t500_pl_120000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_020234_5393fd2_lrb_fnn_mnist_t500_pl_260000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004124_5393fd2_lrb_fnn_mnist_t500_pl_250000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_005012_5393fd2_lrb_fnn_mnist_t500_pl_50000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_033714_5393fd2_lrb_fnn_mnist_t500_pl_290000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034905_5393fd2_lrb_fnn_mnist_t500_pl_180000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234432_5393fd2_lrb_fnn_mnist_t500_pl_60000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034304_5393fd2_lrb_fnn_mnist_t500_pl_100000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_005011_5393fd2_lrb_fnn_mnist_t500_pl_40000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_025232_5393fd2_lrb_fnn_mnist_t500_pl_60000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_062226_5393fd2_lrb_fnn_mnist_t500_pl_220000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234423_5393fd2_lrb_fnn_mnist_t500_pl_250000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_054553_5393fd2_lrb_fnn_mnist_t500_pl_100000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034943_5393fd2_lrb_fnn_mnist_t500_pl_1000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034354_5393fd2_lrb_fnn_mnist_t500_pl_210000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004625_5393fd2_lrb_fnn_mnist_t500_pl_50000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_005720_5393fd2_lrb_fnn_mnist_t500_pl_90000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_032402_5393fd2_lrb_fnn_mnist_t500_pl_220000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_020221_5393fd2_lrb_fnn_mnist_t500_pl_220000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_024012_5393fd2_lrb_fnn_mnist_t500_pl_180000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_052547_5393fd2_lrb_fnn_mnist_t500_pl_180000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_005740_5393fd2_lrb_fnn_mnist_t500_pl_100000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_024142_5393fd2_lrb_fnn_mnist_t500_pl_30000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_020129_5393fd2_lrb_fnn_mnist_t500_pl_100000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_061155_5393fd2_lrb_fnn_mnist_t500_pl_180000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_024448_5393fd2_lrb_fnn_mnist_t500_pl_150000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_004602_5393fd2_lrb_fnn_mnist_t500_pl_280000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_024650_5393fd2_lrb_fnn_mnist_t500_pl_180000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_015651_5393fd2_lrb_fnn_mnist_t500_pl_30000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_013948_5393fd2_lrb_fnn_mnist_t500_pl_70000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_044418_5393fd2_lrb_fnn_mnist_t500_pl_190000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_003827_5393fd2_lrb_fnn_mnist_t500_pl_180000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234432_5393fd2_lrb_fnn_mnist_t500_pl_50000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024228_5393fd2_lrb_fnn_mnist_t500_pl_50000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234429_5393fd2_lrb_fnn_mnist_t500_pl_1000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_013907_5393fd2_lrb_fnn_mnist_t500_pl_20000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_014745_5393fd2_lrb_fnn_mnist_t500_pl_270000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034459_5393fd2_lrb_fnn_mnist_t500_pl_30000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024613_5393fd2_lrb_fnn_mnist_t500_pl_170000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234538_5393fd2_lrb_fnn_mnist_t500_pl_240000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_013708_5393fd2_lrb_fnn_mnist_t500_pl_170000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_030146_5393fd2_lrb_fnn_mnist_t500_pl_140000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_051128_5393fd2_lrb_fnn_mnist_t500_pl_60000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024104_5393fd2_lrb_fnn_mnist_t500_pl_290000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_023811_5393fd2_lrb_fnn_mnist_t500_pl_90000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004442_5393fd2_lrb_fnn_mnist_t500_pl_50000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004334_5393fd2_lrb_fnn_mnist_t500_pl_0_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234425_5393fd2_lrb_fnn_mnist_t500_pl_280000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_052508_5393fd2_lrb_fnn_mnist_t500_pl_140000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034843_5393fd2_lrb_fnn_mnist_t500_pl_130000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024655_5393fd2_lrb_fnn_mnist_t500_pl_190000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234412_5393fd2_lrb_fnn_mnist_t500_pl_160000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_044342_5393fd2_lrb_fnn_mnist_t500_pl_170000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_032316_5393fd2_lrb_fnn_mnist_t500_pl_170000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234501_5393fd2_lrb_fnn_mnist_t500_pl_280000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_050717_5393fd2_lrb_fnn_mnist_t500_pl_160000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034402_5393fd2_lrb_fnn_mnist_t500_pl_220000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_020204_5393fd2_lrb_fnn_mnist_t500_pl_180000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_020227_5393fd2_lrb_fnn_mnist_t500_pl_230000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_030000_5393fd2_lrb_fnn_mnist_t500_pl_130000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_050622_5393fd2_lrb_fnn_mnist_t500_pl_50000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234359_5393fd2_lrb_fnn_mnist_t500_pl_90000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034252_5393fd2_lrb_fnn_mnist_t500_pl_80000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_060443_5393fd2_lrb_fnn_mnist_t500_pl_90000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_050630_5393fd2_lrb_fnn_mnist_t500_pl_60000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_024256_5393fd2_lrb_fnn_mnist_t500_pl_70000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234424_5393fd2_lrb_fnn_mnist_t500_pl_270000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_050824_5393fd2_lrb_fnn_mnist_t500_pl_0_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_014321_5393fd2_lrb_fnn_mnist_t500_pl_180000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234417_5393fd2_lrb_fnn_mnist_t500_pl_190000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004611_5393fd2_lrb_fnn_mnist_t500_pl_1000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004906_5393fd2_lrb_fnn_mnist_t500_pl_130000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_014510_5393fd2_lrb_fnn_mnist_t500_pl_220000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234540_5393fd2_lrb_fnn_mnist_t500_pl_260000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034936_5393fd2_lrb_fnn_mnist_t500_pl_290000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_050829_5393fd2_lrb_fnn_mnist_t500_pl_1000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234522_5393fd2_lrb_fnn_mnist_t500_pl_170000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_044429_5393fd2_lrb_fnn_mnist_t500_pl_210000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_055036_5393fd2_lrb_fnn_mnist_t500_pl_200000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_052743_5393fd2_lrb_fnn_mnist_t500_pl_250000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024912_5393fd2_lrb_fnn_mnist_t500_pl_230000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_055030_5393fd2_lrb_fnn_mnist_t500_pl_190000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_024800_5393fd2_lrb_fnn_mnist_t500_pl_200000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_050730_5393fd2_lrb_fnn_mnist_t500_pl_200000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004611_5393fd2_lrb_fnn_mnist_t500_pl_0_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234501_5393fd2_lrb_fnn_mnist_t500_pl_270000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024959_5393fd2_lrb_fnn_mnist_t500_pl_0_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234504_5393fd2_lrb_fnn_mnist_t500_pl_10000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034928_5393fd2_lrb_fnn_mnist_t500_pl_260000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_014336_5393fd2_lrb_fnn_mnist_t500_pl_200000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234504_5393fd2_lrb_fnn_mnist_t500_pl_1000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_013929_5393fd2_lrb_fnn_mnist_t500_pl_50000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004548_5393fd2_lrb_fnn_mnist_t500_pl_230000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_054907_5393fd2_lrb_fnn_mnist_t500_pl_140000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234429_5393fd2_lrb_fnn_mnist_t500_pl_10000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_020248_5393fd2_lrb_fnn_mnist_t500_pl_290000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234616_5393fd2_lrb_fnn_mnist_t500_pl_70000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034909_5393fd2_lrb_fnn_mnist_t500_pl_200000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034444_5393fd2_lrb_fnn_mnist_t500_pl_10000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234457_5393fd2_lrb_fnn_mnist_t500_pl_230000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234422_5393fd2_lrb_fnn_mnist_t500_pl_240000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234409_5393fd2_lrb_fnn_mnist_t500_pl_130000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_024007_5393fd2_lrb_fnn_mnist_t500_pl_170000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_035646_5393fd2_lrb_fnn_mnist_t500_pl_70000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034437_5393fd2_lrb_fnn_mnist_t500_pl_300000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_034538_5393fd2_lrb_fnn_mnist_t500_pl_70000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004922_5393fd2_lrb_fnn_mnist_t500_pl_150000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_023450_5393fd2_lrb_fnn_mnist_t500_pl_40000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_013559_5393fd2_lrb_fnn_mnist_t500_pl_140000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_013905_5393fd2_lrb_fnn_mnist_t500_pl_10000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034951_5393fd2_lrb_fnn_mnist_t500_pl_20000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_035934_5393fd2_lrb_fnn_mnist_t500_pl_100000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_024319_5393fd2_lrb_fnn_mnist_t500_pl_90000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234510_5393fd2_lrb_fnn_mnist_t500_pl_70000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_033622_5393fd2_lrb_fnn_mnist_t500_pl_280000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_025850_5393fd2_lrb_fnn_mnist_t500_pl_110000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004956_5393fd2_lrb_fnn_mnist_t500_pl_270000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_050750_5393fd2_lrb_fnn_mnist_t500_pl_220000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_043200_5393fd2_lrb_fnn_mnist_t500_pl_300000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_052814_5393fd2_lrb_fnn_mnist_t500_pl_270000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_024127_5393fd2_lrb_fnn_mnist_t500_pl_10000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_053706_5393fd2_lrb_fnn_mnist_t500_pl_20000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_064635_5393fd2_lrb_fnn_mnist_t500_pl_290000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_043109_5393fd2_lrb_fnn_mnist_t500_pl_270000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034707_5393fd2_lrb_fnn_mnist_t500_pl_100000_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_003654_5393fd2_lrb_fnn_mnist_t500_pl_140000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_025154_5393fd2_lrb_fnn_mnist_t500_pl_50000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_064443_5393fd2_lrb_fnn_mnist_t500_pl_270000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_054940_5393fd2_lrb_fnn_mnist_t500_pl_150000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234417_5393fd2_lrb_fnn_mnist_t500_pl_210000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_013857_5393fd2_lrb_fnn_mnist_t500_pl_280000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_013658_5393fd2_lrb_fnn_mnist_t500_pl_160000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_043037_5393fd2_lrb_fnn_mnist_t500_pl_240000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234525_5393fd2_lrb_fnn_mnist_t500_pl_180000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_033856_5393fd2_lrb_fnn_mnist_t500_pl_0_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_020156_5393fd2_lrb_fnn_mnist_t500_pl_140000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_061150_5393fd2_lrb_fnn_mnist_t500_pl_160000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_031113_5393fd2_lrb_fnn_mnist_t500_pl_150000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234337_5393fd2_lrb_fnn_mnist_t500_pl_0_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_040735_5393fd2_lrb_fnn_mnist_t500_pl_200000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034330_5393fd2_lrb_fnn_mnist_t500_pl_160000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_003803_5393fd2_lrb_fnn_mnist_t500_pl_160000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_024026_5393fd2_lrb_fnn_mnist_t500_pl_210000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_034240_5393fd2_lrb_fnn_mnist_t500_pl_60000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_043142_5393fd2_lrb_fnn_mnist_t500_pl_290000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234513_5393fd2_lrb_fnn_mnist_t500_pl_80000_1_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234410_5393fd2_lrb_fnn_mnist_t500_pl_140000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_040817_5393fd2_lrb_fnn_mnist_t500_pl_210000_4_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234428_5393fd2_lrb_fnn_mnist_t500_pl_0_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_044300_5393fd2_lrb_fnn_mnist_t500_pl_130000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_023955_5393fd2_lrb_fnn_mnist_t500_pl_150000_3_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_052642_5393fd2_lrb_fnn_mnist_t500_pl_220000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_005848_5393fd2_lrb_fnn_mnist_t500_pl_120000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_004545_5393fd2_lrb_fnn_mnist_t500_pl_210000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171018_234421_5393fd2_lrb_fnn_mnist_t500_pl_230000_1_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_004536_5393fd2_lrb_fnn_mnist_t500_pl_180000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_024956_5393fd2_lrb_fnn_mnist_t500_pl_300000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_024933_5393fd2_lrb_fnn_mnist_t500_pl_250000_3_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_051106_5393fd2_lrb_fnn_mnist_t500_pl_50000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_060327_5393fd2_lrb_fnn_mnist_t500_pl_60000_5_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004817_5393fd2_lrb_fnn_mnist_t500_pl_70000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_044134_5393fd2_lrb_fnn_mnist_t500_pl_80000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_034438_5393fd2_lrb_fnn_mnist_t500_pl_0_4_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_013714_5393fd2_lrb_fnn_mnist_t500_pl_180000_2_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_014527_5393fd2_lrb_fnn_mnist_t500_pl_230000_2_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_004951_5393fd2_lrb_fnn_mnist_t500_pl_240000_2_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_043827_5393fd2_lrb_fnn_mnist_t500_pl_60000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_052635_5393fd2_lrb_fnn_mnist_t500_pl_210000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_004547_5393fd2_lrb_fnn_mnist_t500_pl_220000_2_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_032323_5393fd2_lrb_fnn_mnist_t500_pl_180000_3_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_020049_5393fd2_lrb_fnn_mnist_t500_pl_60000_3_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_044431_5393fd2_lrb_fnn_mnist_t500_pl_220000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_054616_5393fd2_lrb_fnn_mnist_t500_pl_110000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_034313_5393fd2_lrb_fnn_mnist_t500_pl_110000_4_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_052720_5393fd2_lrb_fnn_mnist_t500_pl_240000_5_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_055025_5393fd2_lrb_fnn_mnist_t500_pl_180000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_043708_5393fd2_lrb_fnn_mnist_t500_pl_40000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_045308_5393fd2_lrb_fnn_mnist_t500_pl_260000_4_400/diary', '/home/users/chunyuan.li/public_results/chun/171019_055152_5393fd2_lrb_fnn_mnist_t500_pl_250000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_004313_5393fd2_lrb_fnn_mnist_t500_pl_290000_1_400/diary', '/home/users/chunyuan.li/public_results/chun/171018_234440_5393fd2_lrb_fnn_mnist_t500_pl_90000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171019_055346_5393fd2_lrb_fnn_mnist_t500_pl_280000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171019_050820_5393fd2_lrb_fnn_mnist_t500_pl_290000_5_50/diary', '/home/users/chunyuan.li/public_results/chun/171019_054331_5393fd2_lrb_fnn_mnist_t500_pl_80000_5_200/diary', '/home/users/chunyuan.li/public_results/chun/171018_234500_5393fd2_lrb_fnn_mnist_t500_pl_250000_1_100/diary', '/home/users/chunyuan.li/public_results/chun/171018_234426_5393fd2_lrb_fnn_mnist_t500_pl_290000_1_50/diary']\n",
      "Error. Can not read config: depth 1, width 50 and dim 40000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 50000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 60000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 70000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 80000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 90000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 100000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 110000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 120000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 130000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 140000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 150000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 160000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 170000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 180000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 190000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 200000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 210000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 220000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 230000.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error. Can not read config: depth 1, width 50 and dim 240000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 250000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 260000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 270000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 280000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 290000.\n",
      "Error. Can not read config: depth 1, width 50 and dim 300000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 50000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 60000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 70000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 80000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 90000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 100000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 110000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 120000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 130000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 140000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 150000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 160000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 170000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 180000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 190000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 200000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 210000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 220000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 230000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 240000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 250000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 260000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 270000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 280000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 290000.\n",
      "Error. Can not read config: depth 2, width 50 and dim 300000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 50000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 60000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 70000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 80000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 90000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 100000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 110000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 120000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 130000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 140000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 150000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 160000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 170000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 180000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 190000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 200000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 210000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 220000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 230000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 240000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 250000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 260000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 270000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 280000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 290000.\n",
      "Error. Can not read config: depth 3, width 50 and dim 300000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 50000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 60000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 70000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 80000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 90000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 100000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 110000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 120000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 130000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 140000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 150000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 160000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 170000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 180000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 190000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 200000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 210000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 220000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 230000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 240000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 250000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 260000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 270000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 280000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 290000.\n",
      "Error. Can not read config: depth 4, width 50 and dim 300000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 50000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 60000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 70000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 80000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 90000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 100000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 110000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 120000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 130000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 140000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 150000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 160000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 170000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 180000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 190000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 200000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 210000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 220000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 230000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 240000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 250000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 260000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 270000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 280000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 290000.\n",
      "Error. Can not read config: depth 5, width 50 and dim 300000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 80000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 90000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 100000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 110000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 120000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 130000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 140000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 150000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 160000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 170000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 180000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 190000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 200000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 210000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 220000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 230000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 240000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 250000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 260000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 270000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 280000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 290000.\n",
      "Error. Can not read config: depth 1, width 100 and dim 300000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 90000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 100000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 110000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 120000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 130000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 140000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 150000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 160000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 170000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 180000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 190000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 200000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 210000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 220000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 230000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 240000.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error. Can not read config: depth 2, width 100 and dim 250000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 260000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 270000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 280000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 290000.\n",
      "Error. Can not read config: depth 2, width 100 and dim 300000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 100000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 110000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 120000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 130000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 140000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 150000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 160000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 170000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 180000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 190000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 200000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 210000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 220000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 230000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 240000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 250000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 260000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 270000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 280000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 290000.\n",
      "Error. Can not read config: depth 3, width 100 and dim 300000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 110000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 120000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 130000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 140000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 150000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 160000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 170000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 180000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 190000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 200000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 210000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 220000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 230000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 240000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 250000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 260000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 270000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 280000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 290000.\n",
      "Error. Can not read config: depth 4, width 100 and dim 300000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 120000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 130000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 140000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 150000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 160000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 170000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 180000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 190000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 200000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 210000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 220000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 230000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 240000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 250000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 260000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 270000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 280000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 290000.\n",
      "Error. Can not read config: depth 5, width 100 and dim 300000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 160000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 170000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 180000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 190000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 200000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 210000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 220000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 230000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 240000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 250000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 260000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 270000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 280000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 290000.\n",
      "Error. Can not read config: depth 1, width 200 and dim 300000.\n",
      "Error. Can not read config: depth 2, width 200 and dim 200000.\n",
      "Error. Can not read config: depth 2, width 200 and dim 210000.\n",
      "Error. Can not read config: depth 2, width 200 and dim 220000.\n",
      "Error. Can not read config: depth 2, width 200 and dim 230000.\n",
      "Error. Can not read config: depth 2, width 200 and dim 240000.\n",
      "Error. Can not read config: depth 2, width 200 and dim 250000.\n",
      "Error. Can not read config: depth 2, width 200 and dim 260000.\n",
      "Error. Can not read config: depth 2, width 200 and dim 270000.\n",
      "Error. Can not read config: depth 2, width 200 and dim 280000.\n",
      "Error. Can not read config: depth 2, width 200 and dim 290000.\n",
      "Error. Can not read config: depth 2, width 200 and dim 300000.\n",
      "Error. Can not read config: depth 3, width 200 and dim 240000.\n",
      "Error. Can not read config: depth 3, width 200 and dim 250000.\n",
      "Error. Can not read config: depth 3, width 200 and dim 260000.\n",
      "Error. Can not read config: depth 3, width 200 and dim 270000.\n",
      "Error. Can not read config: depth 3, width 200 and dim 280000.\n",
      "Error. Can not read config: depth 3, width 200 and dim 290000.\n",
      "Error. Can not read config: depth 3, width 200 and dim 300000.\n",
      "Error. Can not read config: depth 4, width 200 and dim 280000.\n",
      "Error. Can not read config: depth 4, width 200 and dim 290000.\n",
      "Error. Can not read config: depth 4, width 200 and dim 300000.\n"
     ]
    }
   ],
   "source": [
    "results_dir = '/home/users/chunyuan.li/public_results/chun'\n",
    "       \n",
    "depth = [1,2,3,4,5]\n",
    "width = [50,100,200,400]\n",
    "dim   = [0,1000,10000,20000,30000,40000,50000,60000,70000,80000,90000,100000,110000,120000,130000,140000,150000,160000,170000,180000,190000,200000,210000,220000,230000,240000,250000,260000,270000,280000,290000,300000]\n",
    "# dim =[0]\n",
    "\n",
    "########## 1. filename list of diary ########################\n",
    "diary_names = []\n",
    "for subdir, dirs, files in os.walk(results_dir):\n",
    "    if '5393fd2_lrb_fnn_mnist_t500_pl' in subdir:\n",
    "        for subdir, dirs, files in os.walk(subdir):\n",
    "            for file in files:\n",
    "                if file == 'diary':\n",
    "                    fname = os.path.join(subdir, file)\n",
    "                    diary_names.append(fname)\n",
    "            \n",
    "print diary_names\n",
    "########## 2. Construct stats (width, depth, dim) ##########\n",
    "# acc_test_all : Tensor (width, depth, dim)\n",
    "# num_param_all: Tensor (width, depth)\n",
    "# acc_solved_all:  Tensor (width, depth)\n",
    "# dim_solved_all:  Tensor (width, depth)\n",
    "############################################################\n",
    "nw, nd, nn= len(width), len(depth), len(dim)\n",
    "\n",
    "acc_test_all  = np.zeros((len(width), len(depth), len(dim)))\n",
    "num_param_all = np.zeros((len(width), len(depth)))\n",
    "acc_solved_all = np.zeros((len(width), len(depth)))\n",
    "dim_solved_all = np.zeros((len(width), len(depth)))\n",
    "\n",
    "mode = 3         # {0: test loss, 1: test acc}\n",
    "error_files = [] #  record the error file\n",
    "\n",
    "# 2.1 construct acc_test_all and num_param_all\n",
    "for id_w in range(len(width)):\n",
    "    w = width[id_w]\n",
    "    for id_ll in range(len(depth)):\n",
    "        ll = depth[id_ll]\n",
    "        for id_d in range(len(dim)):\n",
    "            d = dim[id_d]\n",
    "            \n",
    "            # 2.1.1 Read the results, \n",
    "            for f in diary_names:\n",
    "                if '_'+str(d)+'_'+str(ll)+'_'+str(w)+'/' in f:\n",
    "                    # print \"%d is in\" % d + f\n",
    "                    \n",
    "                    with open(f,'r') as ff:\n",
    "                        lines0 = ff.readlines()\n",
    "                        try:\n",
    "                            R = extract_num(lines0)\n",
    "                            R = np.array(R)\n",
    "\n",
    "                        except ValueError:\n",
    "                            error_files.append((w,ll,d))\n",
    "                            R = np.zeros(len(R))\n",
    "                            print \"Error. Can not read config: depth %d, width %d and dim %d.\" % (ll, w, d) \n",
    "                            # break\n",
    "                            \n",
    "                            \n",
    "            # 2.1.2 Assign the results\n",
    "            r = R[mode]  \n",
    "            acc_test_all[id_w,id_ll,id_d]=r\n",
    "            if d==0:\n",
    "                num_param_all[id_w,id_ll]=parse_num_params(lines0) \n",
    "                \n",
    "# 2.2 construct acc_solved_all and dim_solved_all           \n",
    "for id_w in range(len(width)):\n",
    "    w = width[id_w]\n",
    "    for id_ll in range(len(depth)):\n",
    "        ll = depth[id_ll]\n",
    "        for id_d in range(len(dim)):\n",
    "            d = dim[id_d]\n",
    "            \n",
    "            r = acc_test_all[id_w,id_ll,id_d]\n",
    "            if d==0:\n",
    "                test_acc_bl = r # 1.0 # r        \n",
    "                # print \"Acc goal is: \" + str(test_acc_sl) + \" for network with depth \" + str(ll) + \" width \"+ str(w)\n",
    "            else:\n",
    "                test_acc = r\n",
    "                if test_acc>test_acc_bl*0.9:\n",
    "                    acc_solved_all[id_w,id_ll]=test_acc\n",
    "                    dim_solved_all[id_w,id_ll]=d\n",
    "                    # print \"Intrinsic dim is: \" + str(d) + \" for network with depth \" + str(ll) + \" width \"+ str(w)\n",
    "                    # print \"\\n\"\n",
    "                    break\n",
    "                    \n",
    "\n",
    "########## 3. Construct Tensors for Analysis (width, depth, dim) ##########                    \n",
    "acc_base  = acc_test_all[:,:,0]\n",
    "acc_solve = acc_base*0.9\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.27594,  0.31596,  0.3499 ,  0.36754,  0.38018],\n",
       "       [ 0.3715 ,  0.47786,  0.57946,  0.67088,  0.74454],\n",
       "       [ 0.53088,  0.80302,  0.96492,  0.9866 ,  0.98634],\n",
       "       [ 0.75504,  0.98788,  0.99904,  0.99666,  0.99444]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_test_all[:,:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.     ,  0.     ,  0.     ,  0.     ,  0.     ],\n",
       "       [ 0.     ,  0.     ,  0.     ,  0.     ,  0.     ],\n",
       "       [ 0.     ,  0.     ,  0.     ,  0.     ,  0.94928],\n",
       "       [ 0.64388,  0.85058,  0.95746,  0.98312,  0.99086]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_test_all[:,:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0.99444],[1000, 0.11292],[10000, 0.15122],[20000, 0.19618],[30000, 0.24556],[40000, 0.29172],[50000, 0.3347],[60000, 0.3811],[70000, 0.42188],[80000, 0.46698],[90000, 0.50712],[100000, 0.54436],[110000, 0.5939],[120000, 0.63058],[130000, 0.67804],[140000, 0.71868],[150000, 0.7643],[160000, 0.80814],[170000, 0.84078],[180000, 0.87192],[190000, 0.90398],[200000, 0.92432],[210000, 0.94128],[220000, 0.95406],[230000, 0.96462],[240000, 0.97232],[250000, 0.97732],[260000, 0.9809],[270000, 0.98446],[280000, 0.98714],[290000, 0.98706],[300000, 0.99086]\n"
     ]
    }
   ],
   "source": [
    "i,j=3,4\n",
    "dim_, acc_train_ = dim, acc_test_all[i,j,:]\n",
    "print ','.join(['[%i, %s]' % (dim_[n], acc_train_[n]) for n in xrange(len(dim_))])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  39760.,   42310.,   44860.,   47410.,   49960.],\n",
       "       [  79510.,   89610.,   99710.,  109810.,  119910.],\n",
       "       [ 159010.,  199210.,  239410.,  279610.,  319810.],\n",
       "       [ 318010.,  478410.,  638810.,  799210.,  959610.]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_param_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAFYCAYAAACroXBwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9ccae7590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i,j=3,4\n",
    "\n",
    "fig, ax = subplots(figsize=(6,5) )\n",
    "  \n",
    "font = {'size'   : 12}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "plot(dim[1:], acc_test_all[i,j,1:], 'o', mec='b', mfc=(.8,.8,1), ms=10)\n",
    "plot(dim_solved_all[i,j], acc_solved_all[i,j], 'o', mec='b', mfc='b', ms=10)\n",
    "axhline(acc_test_all[i,j,0], ls='-', color='k',label='baseline')\n",
    "axhline(acc_test_all[i,j,0] * .9, ls=':', color='k',label='solved')\n",
    "plt.legend()\n",
    "ax.set_xlabel('Subspace dimension $d$')\n",
    "ax.set_ylabel('Validation accuracy')\n",
    "\n",
    "ax.set_title('width %d, depth %d' %(width[i], depth[j]))\n",
    "plt.grid()\n",
    "ax.set_ylim([0.0,1.00])\n",
    "        \n",
    "# fig.savefig(\"figs/fnn_mnistPL_W\"+str(width[i])+\"_L\"+str(depth[j])+\".pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABKMAAAOuCAYAAADbw9RBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e22611dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,16))\n",
    "fig.subplots_adjust(hspace=0.4)\n",
    "\n",
    "acc_solved_all\n",
    "\n",
    "for i in range(nw):\n",
    "    for j in range(nd):\n",
    "        id = i*nd+j+1\n",
    "        ax = plt.subplot(nw, nd, id)\n",
    "\n",
    "        plot(dim[1:], acc_test_all[i,j,1:], 'o', mec='b', mfc=(.8,.8,1), ms=10)\n",
    "        plot(dim_solved_all[i,j], acc_solved_all[i,j], 'o', mec='b', mfc='b', ms=10)\n",
    "        axhline(acc_test_all[i,j,0], ls='-', color='k')\n",
    "        axhline(acc_test_all[i,j,0] * .9, ls=':', color='k')\n",
    "        ax.set_xlabel('Subspace dim $d$')\n",
    "        if j==0:\n",
    "            ax.set_ylabel('Training accuracy')\n",
    "            \n",
    "        ax.set_title('width %d, depth %d' %(width[i], depth[j]))\n",
    "        plt.grid()\n",
    "        ax.set_ylim([0.1,1.01])\n",
    "        \n",
    "fig.savefig(\"figs/fnn_mnist_pl_all_configs.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAH8CAYAAABb8GvmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e21384cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def figsize(width,height):\n",
    "    rcParams['figure.figsize'] = (width,height)\n",
    "\n",
    "font = {'size'   : 22}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "\n",
    "\n",
    "n_dim = len(dim) \n",
    "dim3 = np.repeat(dim, 4*5, axis=0).reshape([4, 5, n_dim],order='F')\n",
    "\n",
    "\n",
    "param = num_param_all.reshape(20)\n",
    "acc_direct = acc_test_all[:,:,0].reshape(20)\n",
    "\n",
    "direct_md =  np.array([ (int(param[i]), acc_direct[i]) for i in range(20) ])\n",
    "\n",
    "acc_test_md, num_trainable_md = acc_test_all.reshape(20*n_dim) , dim3.reshape(20*n_dim) \n",
    "subspace_md =  np.array([(int(num_trainable_md[i]), acc_test_md[i]) for i in range(20*n_dim) if (num_trainable_md[i]!=0 and acc_test_md[i]!=0.0)])\n",
    "\n",
    "figsize(15, 8)\n",
    "semilogx(subspace_md[:,0], subspace_md[:,1], 'o', mfc=(.7,.7,1), mec='k', ms=14, label='Subspace')\n",
    "semilogx(direct_md[:,0], direct_md[:,1], 'o', mfc=(.8,.8,.8), mec='k', ms=14, label='Direct')\n",
    "\n",
    "xlabel('Number of trainable parameters')\n",
    "ylabel('Training accuracy')\n",
    "plt.legend()\n",
    "savefig('figs/mnist_pl_trainable_cmp_log.pdf', bbox_inches='tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8AAAAH3CAYAAAB0NjanAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGUEJsIiIiIiIiGQEJcAiIiIiIiKSEf4/JbUEHfGtOg8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e213adbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(15, 8)\n",
    "plot(subspace_md[:,0], subspace_md[:,1], 'o', mfc=(.7,.7,1), mec='k', ms=14, label='Subspace')\n",
    "plot(direct_md[:,0], direct_md[:,1], 'o', mfc=(.8,.8,.8), mec='k', ms=14, label='Direct')\n",
    "xlabel('Number of trainable parameters')\n",
    "ylabel('Training accuracy')\n",
    "plt.legend()\n",
    "# savefig('mnist_small_direct.png')\n",
    "savefig('figs/mnist_pl_trainable_cmp.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of diary files extracted: 33\n"
     ]
    }
   ],
   "source": [
    "results_dir = '/home/users/chunyuan.li/public_results/chun/results_pl_pd2'\n",
    "       \n",
    "depth = [5]\n",
    "width = [400]\n",
    "\n",
    "########## 1. filename list of diary ########################\n",
    "percentage_data = 0.5                    \n",
    "# filename list of diary\n",
    "diary_names = []\n",
    "for subdir, dirs, files in os.walk(results_dir):\n",
    "    if (\"5_400_\"+str(percentage_data) in subdir):\n",
    "        # print subdir\n",
    "        for file in files:\n",
    "            if file == 'diary':\n",
    "                fname = os.path.join(subdir, file)\n",
    "                if int(fname.split('/')[-2].split('_')[-3]) == 5:\n",
    "                    diary_names.append(fname)\n",
    "                    \n",
    "\n",
    "print \"Number of diary files extracted: \" + str(len(diary_names))\n",
    "# print diary_names\n",
    "\n",
    "dim =  []\n",
    "for f in diary_names:\n",
    "    d = int(f.split('/')[-2].split('_')[-4])\n",
    "    dim.append(d)\n",
    "dim = sorted(dim)\n",
    "    \n",
    "dim = sorted(list(set(dim)))\n",
    "\n",
    "\n",
    "########## 2. Construct stats (width, depth, dim) ##########\n",
    "# acc_test_all : Tensor (width, depth, dim)\n",
    "# num_param_all: Tensor (width, depth)\n",
    "# acc_solved_all:  Tensor (width, depth)\n",
    "# dim_solved_all:  Tensor (width, depth)\n",
    "############################################################\n",
    "nn= len(dim)\n",
    "\n",
    "acc_test_all  = np.zeros(len(dim))\n",
    "\n",
    "mode = 3         # {0: test loss, 1: test acc}\n",
    "error_files = [] #  record the error file\n",
    "\n",
    "# 2.1 construct acc_test_all and num_param_all\n",
    "\n",
    "for id_d in range(len(dim)):\n",
    "    d, ll, w = dim[id_d],depth[0],width[0]\n",
    "\n",
    "    # 2.1.1 Read the results, \n",
    "    for f in diary_names:\n",
    "        if '_'+str(d)+'_'+str(ll)+'_'+str(w)+'_'+str(percentage_data)+'/' in f:\n",
    "            #print \"%d is in\" % d + f\n",
    "\n",
    "            with open(f,'r') as ff:\n",
    "                lines0 = ff.readlines()\n",
    "                try:\n",
    "                    R = extract_num(lines0)\n",
    "                    R = np.array(R)\n",
    "                    # print R\n",
    "\n",
    "                except ValueError:\n",
    "                    error_files.append((w,ll,d))\n",
    "                    R = np.zeros(len(R))\n",
    "                    print \"Error. Can not read config: depth %d, width %d and dim %d.\" % (ll, w, d) \n",
    "                    # break\n",
    "\n",
    "\n",
    "    # 2.1.2 Assign the results\n",
    "    r = R[mode]  \n",
    "    acc_test_all[id_d]=r\n",
    "    if d==0:\n",
    "        num_param_all=parse_num_params(lines0) \n",
    "                \n",
    "# 2.2 construct acc_solved_all and dim_solved_all           \n",
    "\n",
    "for id_d in range(len(dim)):\n",
    "    d = dim[id_d]\n",
    "    r = acc_test_all[id_d]\n",
    "    if d==0:\n",
    "        test_acc_bl = r # 1.0 # r        \n",
    "        # print \"Acc goal is: \" + str(test_acc_sl) + \" for network with depth \" + str(ll) + \" width \"+ str(w)\n",
    "    else:\n",
    "        test_acc = r\n",
    "        if test_acc>test_acc_bl*0.9:\n",
    "            acc_solved_all=test_acc\n",
    "            dim_solved_all=d\n",
    "            # print \"Intrinsic dim is: \" + str(d) + \" for network with depth \" + str(ll) + \" width \"+ str(w)\n",
    "            # print \"\\n\"\n",
    "            break\n",
    "                    \n",
    "\n",
    "########## 3. Construct Tensors for Analysis (width, depth, dim) ##########                    \n",
    "acc_base  = acc_test_all[0]\n",
    "acc_solve = acc_base*0.9\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 110000, 120000, 130000, 140000, 150000, 160000, 170000, 180000, 190000, 200000, 210000, 220000, 230000, 240000, 250000, 260000, 270000, 280000, 290000, 300000]\n",
      "[ 0.99156  0.11652  0.1764   0.23804  0.29904  0.3604   0.42488  0.48488\n",
      "  0.54584  0.605    0.66688  0.73308  0.79204  0.84436  0.89172  0.92576\n",
      "  0.95044  0.96692  0.97468  0.98328  0.98708  0.99308  0.9946   0.99728\n",
      "  0.9982   0.99884  0.99884  0.99724  0.99804  0.9996   0.99988  0.99892]\n",
      "[0, 0.99156],[1000, 0.11652],[10000, 0.1764],[20000, 0.23804],[30000, 0.29904],[40000, 0.3604],[50000, 0.42488],[60000, 0.48488],[70000, 0.54584],[80000, 0.605],[90000, 0.66688],[100000, 0.73308],[110000, 0.79204],[120000, 0.84436],[130000, 0.89172],[140000, 0.92576],[150000, 0.95044],[160000, 0.96692],[170000, 0.97468],[180000, 0.98328],[190000, 0.98708],[200000, 0.99308],[210000, 0.9946],[220000, 0.99728],[230000, 0.9982],[240000, 0.99884],[250000, 0.99884],[260000, 0.99724],[270000, 0.99804],[280000, 0.9996],[290000, 0.99988],[300000, 0.99892]\n"
     ]
    }
   ],
   "source": [
    "print dim\n",
    "print acc_test_all\n",
    "\n",
    "print ','.join(['[%i, %s]' % (dim[n], acc_test_all[n]) for n in xrange(len(dim))])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.01)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAFYCAYAAACroXBwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9ccc65d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i,j=3,2\n",
    "\n",
    "fig, ax = subplots(figsize=(6,5) )\n",
    "  \n",
    "font = {'size'   : 12}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "plot(dim[1:], acc_test_all[1:], 'o', mec='b', mfc=(.8,.8,1), ms=10)\n",
    "# plot(dim_solved_all, acc_solved_all, 'o', mec='b', mfc='b', ms=10)\n",
    "axhline(acc_test_all[0], ls='-', color='k',label='baseline')\n",
    "axhline(acc_test_all[0] * .9, ls=':', color='k',label='solved')\n",
    "plt.legend()\n",
    "ax.set_xlabel('Subspace dimension $d$')\n",
    "ax.set_ylabel('Training accuracy')\n",
    "\n",
    "ax.set_title('Percentage of training data set: %.2f' % percentage_data)\n",
    "plt.grid()\n",
    "ax.set_ylim([0.0,1.01])\n",
    "        \n",
    "# fig.savefig(\"figs/fnn_mnistPL_W\"+str(width[i])+\"_L\"+str(depth[j])+\".pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.212812594904\n",
      "0.212667187512\n",
      "0.270141089437\n",
      "0.227242285477\n",
      "Entrop of Y:\n",
      "2.30258509299\n",
      "2.30101181725\n",
      "2.30084384767\n",
      "2.30119879622\n"
     ]
    }
   ],
   "source": [
    "prob5 = [0.09776, 0.11276, 0.09932, 0.1028, 0.097, 0.0922, 0.0958, 0.10408, 0.09876, 0.09952]\n",
    "prob1 = [0.0974, 0.1128, 0.103, 0.1038, 0.0948, 0.094, 0.0912, 0.102, 0.098, 0.103]\n",
    "prob0 = [0.1 for i in range(10)]\n",
    "prob_ = [0.09864, 0.11356, 0.09936, 0.10202, 0.09718, 0.09012, 0.09902, 0.1035, 0.09684, 0.09976]\n",
    "\n",
    "import math\n",
    "def entropy(prob):\n",
    "    return sum([ -p* math.log(p) for p in prob])\n",
    "\n",
    "print entropy(prob0)/math.log(50000)\n",
    "print entropy(prob_)/math.log(50000)\n",
    "print entropy(prob1)/math.log(5000)\n",
    "print entropy(prob5)/math.log(25000)\n",
    "\n",
    "print \"Entrop of Y:\"\n",
    "print entropy(prob0)\n",
    "print entropy(prob_)\n",
    "print entropy(prob1)\n",
    "print entropy(prob5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
